1. Field of the Invention
This invention relates to computer systems and, more particularly, to multi-stage file replication techniques within distributed data management systems.
2. Description of the Related Art
Business data for large corporations and government entities is often spread across multiple physical locations and data centers. A given enterprise company may employ tens of thousands of employees around the world, distributed across tens or hundreds of offices. Many employees from different locations often have to access and/or update the same underlying data, some of which may have to be obtained across relatively slow network connections such as wide area networks (WANs), metropolitan area networks (MANs), and the like. The problem of designing scalable large scale distributed data management systems to support today's distributed enterprises is becoming even more complicated as the total amount of enterprise data rapidly increases to terabyte levels, while continuing financial constraints and industry-wide trends to become more standards-compliant restrict the ability to, for example, deploy proprietary or customized high-speed networks and/or protocols.
One common approach used for data sharing across long distances is to replicate data locally: e.g., in a distributed software development environment with several software engineering sites, data objects such as source code files, design documents, status documents etc. may be replicated at each of the several sites. The volume of the data to be shared, especially when combined with high frequency of updates and large numbers of potential replication sites, however, may make brute-force replication of the needed data impractical. Traditional data replication systems may suffer from various types of limitations. For example, traditional replication schemes involving synchronous replication of files being updated may not be appropriate when the synchronous replication has to be performed over slow wide area network links. Some replication schemes may be too centralized for large scale systems spread over hundreds or thousands of servers and workstations: e.g., if a single server is responsible for propagating updates to a given file across the entire network, the server may quickly become a performance bottleneck and/or an availability risk. Replication schemes that attempt to maintain strict replica coherency, e.g., schemes that require that only the single latest version of a replicated data object be accessed at all replication sites, may also not scale well in large networks, since coherency-related traffic may potentially dominate the network linking the sites, and coherency-related delays may significantly affect user response times. The problem of efficiently providing the data needed at various sites in distributed data management systems with reasonable levels of performance is non-trivial even under normal operating conditions, e.g., in the absence of errors and failures. The requirement to respond effectively to failures such as transient or long-lasting system and network outages, which may, for example, potentially result in a disruption of an ongoing replication data stream, or in a node of a cluster becoming unavailable for hours, only complicates the problem further.
Techniques used to alleviate one class of problem in large-scale distributed data management may sometimes tend to aggravate other classes of problems—e.g., increasing the number of replication sites for a file in an effort to reduce access latencies may require extra work for maintaining coherency among the newly added replicas. Large-scale distributed data management systems that employ replication may therefore have to take into consideration various tradeoffs between potentially conflicting goals such as high performance, replica coherency, high availability, and ease of use.